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% Contact: koller@ling.uni-potsdam.de, yusuke@nii.ac.jp
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%%e.agirre@ehu.es or Sergi.Balari@uab.es
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\documentclass[11pt]{article}
\usepackage{acl2014}
\usepackage{times}
\usepackage{url}
\usepackage{latexsym}
\usepackage{graphics,qtree}
\usepackage{multirow}
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\usepackage[normalem]{ulem}
\usepackage{amsmath}

%\setlength\titlebox{5cm}

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\title{Generating Subjective  Responses  to Opinionated Articles in Social Media: \\An Agenda-Driven  Architecture and a Turing-Like Test}

 \author{Tomer Cagan \\
  \normalsize School of Computer Science \\
  \normalsize The Interdisciplinary Center \\
  \normalsize Herzeliya, Israel  \\
  \normalsize {\tt cagan.tomer@idc.ac.il} \\\And
  Stefan L. Frank \\
  \normalsize Centre for Language Studies \\
  \normalsize Radboud University  \\
  \normalsize Nijmegen, The Netherlands \\
  \normalsize {\tt s.frank@let.ru.nl } \\\And
  Reut Tsarfaty \\
  \normalsize Mathematics and Computer Science \\
  \normalsize Weizmann Institute of Science \\
  \normalsize Rehovot, Israel  \\
  \normalsize {\tt tsarfaty@weizmann.ac.il}  \\}

\date{}

\begin{document}
\maketitle
%\begin{abstract}
%With the surge of interest in online human interaction,   user-generated content  in social media is gaining %ever-increasing exposure.  Natural language traffic in social media (e.g., blogs,  microblogs, talkbacks) currently enjoys %vast  automatic monitoring and {\em analysis} efforts, but the question  whether computer systems can   {\em generate} %such content to effectively interact with human agents has not been tackled yet.  This paper presents an  architecture for   %generating  subjective responses  to opinionated  articles, as well as an empirical evaluation of  the responses' %efficacy. Our    architecture integrates  topic and sentiment analysis,   user agenda and a knowledge graph. An evaluation %by human readers shows, among others, that responses  generated using additional world knowledge in the input are regarded %as more human-like than those that rely on  topic, sentiment and agenda only, whereas the use of world knowledge does not %affect perceived relevance.
%\end{abstract}

\input{Abstract.tex}

\input{Introduction.tex}

\input{Approach.tex}

\input{Formal_settings.tex}

\input{Architecture.tex}

\input{Evaluation.tex}

%\input{Qualitative_analysis.tex}

\input{Future_work.tex}

\input{Conclusion.tex}

\bibliography{talkbackref}
\bibliographystyle{acl}

\end{document} 